Cell controller for finding cause of abnormality in manufacturing machine

ABSTRACT

A cell controller includes an inside information acquiring unit for acquiring the inside information of the plurality of manufacturing machines, and an inside information comparing unit which compares, with regard to a first manufacturing machine and a second manufacturing machine, which are selected by the comparison object selecting unit, first inside information and second inside information, which are acquired, and the inside information comparing unit extracting a difference therebetween. The cell controller also includes an abnormality cause finding unit for finding a cause of an abnormality that occurs in the first manufacturing machine or the second manufacturing machine, based on the difference, and an abnormality cause conveying unit for conveying the cause of the abnormality to the outside of the cell controller.

RELATED APPLICATIONS

The present application claims priority to Japanese Patent ApplicationNumber 2016-016575 filed Jan. 29, 2016, the disclosure of which ishereby incorporated by reference herein in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a cell controller for controlling aplurality of manufacturing machines constituting a manufacturing cell.

2. Description of the Related Art

In manufacturing factories, manufacturing machines, for example, machinetools or robots perform operations, such as, processing or welding ofparts. In order to manufacture products, a plurality of manufacturingmachines constitutes a manufacturing line, for example, a manufacturingcell. In this instance, the manufacturing machines constituting themanufacturing cell are controlled by a cell controller via a networkcommunication. The cell controller drives each manufacturing machinebased on commands from a production management device as a hostcomputer.

In such a manufacturing cell, an abnormality occurs in a manufacturingmachine, such as a robot, and thus, the manufacturing machine does notoperate normally in some cases. Thus, the productivity reduces. In orderto solve such a problem, various methods for early detection ofabnormalities in manufacturing machines have been proposed.

For example, Japanese Patent Application Laid-open No. 2004-202624discloses a device for collecting information from a plurality of robotsconnected via a network. Such a device predicts a robot which may breakdown, when a predetermined robot breaks down, by comparing thepreviously registered information on the robots with the real-timeindividual information on the robots. Japanese Patent ApplicationLaid-open No. 2004-202624 also discloses that, based on the informationon the robot predicted to break down, candidate parts necessary when therobot breaks down are extracted.

Japanese Patent No. 4739556 discloses an abnormality determining devicehaving a simulator which uses a model identical to an actual robot, toreproduce the operation and state of the actual robot. This devicecompares the operation and state of the actual robot in response to anoperation command with results of the reproduction made by thesimulator, thereby determining whether an abnormality occurs in theoperation and state of the actual robot.

In order to improve productivity, in general, when an abnormality occursin a manufacturing machine, a cause of the abnormality should be earlydetermined to quickly recover the manufacturing machine.

However, when the abnormality is caused by, for example, an impropersetting or improper operation, it takes a lot of time to determine thecause of the abnormality.

In other words, the abnormality caused by an improper setting orimproper operation can often be found by comparing the informationobtained from a manufacturing machine that operates normally with theinformation obtained from a manufacturing machine in which theabnormality occurs. However, under present circumstances, a maintenancemanager or a support engineer in a machinery manufacture needs toperform a series of operations including picking up the insideinformation of each manufacturing machine and comparing pieces of theinside information with each other, and accordingly, it takes a lot oftime to determine the cause of the abnormality.

Thus, a technology for early detection of an abnormality which may becaused by the inside information of a manufacturing machine, such as animproper setting or an improper operation as described above, has beendesired.

Note that, the device disclosed in Japanese Patent Application Laid-openNo. 2004-202624 predicts a failure in a component of a manufacturingmachine, such as a robot, but cannot predict the occurrence of anabnormality caused by an improper setting or an improper operation in amanufacturing machine, i.e., the inside information of the manufacturingmachine. The same is true in the device disclosed in Japanese Patent No.4739556. Further, in the device disclosed in Japanese Patent No.4739556, if simulation errors occur when the simulator reproduces theoperation and state of the actual robot in response to an operationcommand, the correctness of the results of abnormality determinationreduces.

SUMMARY OF THE INVENTION

The present invention provides a cell controller which efficiently findsan abnormality caused by the inside information of a manufacturingmachine.

According to a first aspect of the present invention, there is provideda cell controller for controlling a plurality of manufacturing machinesconstituting a manufacturing cell. The cell controller includes aninside information acquiring unit which is configured to acquire theinside information of the plurality of manufacturing machines, acomparison object selecting unit which is configured to select, when anabnormality occurs in a first manufacturing machine of the plurality ofmanufacturing machines, the first manufacturing machine and a secondmanufacturing machine which has components similar to those of the firstmanufacturing machine and which normally operates, from among theplurality of manufacturing machines,

an inside information comparing unit which is compare, with regard tothe first manufacturing machine and the second manufacturing machine,which are selected by the comparison object selecting unit, the firstinside information of the first manufacturing machine and the secondinside information of the second manufacturing machine, which areacquired by the inside information acquiring unit, and the insideinformation comparing unit extracting a difference therebetween,

an abnormality cause finding unit which is configured to find a cause ofan abnormality that occurs in the first manufacturing machine or a causeof an abnormality that may occur in the future, based on the differencebetween the first inside information and the second inside information,which is extracted by the inside information comparing unit, and

an abnormality cause conveying unit which is configured to convey thecause of the abnormality, which is found by the abnormality causefinding unit, to the outside of the cell controller.

According to a second aspect of the present invention, in the cellcontroller according to the first aspect, the inside information of eachmanufacturing machine includes at least one of a drive parameterassociated with driving of a manufacturing machine, a function parameterassociated with the function of a manufacturing machine, an operationprogram to be executed by a manufacturing machine, and an operationcommand log obtained by recording, in time series, operation commandsreceived to cause a manufacturing machine to perform a predeterminedoperation.

According to a third aspect of the present invention, the cellcontroller according to the second aspect further includes an abnormalportion correcting unit which is configured to correct, when at leastone of the drive parameter, the function parameter, and the operationprogram is a cause of the abnormality, a portion having the abnormality.

According to a fourth aspect of the present invention, in the cellcontroller according to the third aspect, the operation command log isinformation obtained by recording, in time series, operator'soperations, operation program executing processes, input of signals tothe outside, or operation commands generated by input of signals fromthe outside.

According to a fifth aspect of the present invention, the cellcontroller according to the second aspect further includes an operationcommand complementing unit which is configured to complement, when alack of either an operation command log in the first inside informationor an operation command log in the second inside information causes theabnormality, an operation according to an operation command lacking inthe operation command log, with respect to the manufacturing machinewhich lacks the operation command log.

According to a sixth aspect of the present invention, in the cellcontroller according to any of the first to fifth aspects, thecomparison object selecting unit which is configured to refer to deviceconfiguration information representing components of a manufacturingmachine, which is associated with each of the manufacturing machines,and compares pieces the device configuration information with eachother, thereby selecting the first manufacturing machine and the secondmanufacturing machine.

According to a seventh aspect of the present invention, in the cellcontroller according to any of the first to fifth aspects, thecomparison object selecting unit which is configured to select the firstmanufacturing machine and the second manufacturing machine, based onregistration information obtained by previously correlating the firstmanufacturing machine and the second manufacturing machine, which aresimilar to each other.

According to an eighth aspect of the present invention, in the cellcontroller according to any of the first to seventh aspects, the insideinformation acquiring unit which is configured to acquire the insideinformation of the manufacturing machines at a predetermined interval.

According to a ninth aspect of the present invention, in the cellcontroller according to any of the first to eighth aspects, the cellcontroller further comprises a database which is configured to correlatethe state of an abnormality that occurs in each of the manufacturingmachines and a cause of the occurrence of the abnormality, and storesthe same, and the abnormality cause finding unit is configured to referto the database, thereby finding a cause of an abnormality that occursin the first manufacturing machine, or a cause of an abnormality thatmay occur in the future.

According to a tenth aspect of the present invention, the cellcontroller according to the ninth aspect further includes a databaseupdating unit which is configured to reflect in the database, the stateof an abnormality that occurs in either the first manufacturing machineor the second manufacturing machine, and a difference between the firstinside information and the second inside information, which is extractedby the inside information comparing unit and which causes theabnormality.

According to an eleventh aspect of the present invention, in the cellcontroller according to the ninth aspect, the cell controller is one ofa plurality of cell controllers, and further comprises a databasesharing unit for sharing the database with the other cell controllers.

According to a twelfth aspect of the present invention, the cellcontroller according to the ninth aspect further includes a learninginstrument which is configured to perform machine learning using theinformation stored in the database, the acquired inside information ofeach of the manufacturing machines, and the device configurationinformation representing components of each of the manufacturingmachines, in order to update the information stored in the database.

BRIEF DESCRIPTION OF THE DRAWINGS

These objects, features, and advantages of the present invention andother objects, features, and advantages will become further clearer fromthe detailed description of typical embodiments illustrated in theappended drawings.

FIG. 1 is a block diagram schematically illustrating a production systemprovided with a cell controller according to an embodiment.

FIG. 2 is a block diagram more specifically illustrating the cellcontrol system of the production system shown in FIG. 1.

FIG. 3 is a schematic diagram of a neuron model.

FIG. 4 is a schematic diagram of a three-layered neural network.

DETAILED DESCRIPTION

Embodiments of the present invention will be described below withreference to the drawings. In the following figures, similar members aredesignated with the same reference numerals. These figures are properlymodified in scale to assist the understanding thereof. Further, theembodiments illustrated in the drawings are examples for carrying outthe present invention, and the present invention is not limited to theillustrated embodiments.

FIG. 1 is a block diagram simply illustrating a production systemprovided with a cell controller according to an embodiment.

With reference to FIG. 1, a production system 10 is provided with atleast one manufacturing cell 11, a cell controller 12 for controllingthe manufacturing cell 11, and a production management device 13.

The manufacturing cell 11 is disposed in a factory for manufacturingproducts. In contrast, the cell controller 12 and the productionmanagement device 13 are disposed in a building different from thefactory. For example, the cell controller 12 may be disposed in anotherbuilding within the factory site in which the manufacturing cell 11 isdisposed. In this instance, it is preferable that the manufacturing cell11 and the cell controller 12 are interconnected so that they cancommunicate with each other via a communication device 31, for example,an intranet.

The production management device 13 may, for example, be disposed in anoffice away from the factory. In this instance, it is preferable thatthe cell controller 12 and the production management device 13 areinterconnected so that they can communicate with each other via acommunication device 32, for example, the Internet. Further, it ispreferable that a host computer for managing the working situation of aplurality of manufacturing cells 11 or manufacturing machines in theoffice is applied to the production management device 13 according tothe present embodiment.

The manufacturing cell 11 is a set obtained by flexibly assembling aplurality of manufacturing machines for manufacturing products. Themanufacturing cell 11 according to the present embodiment is constructedby a plurality of manufacturing machines including a machine tool M1, arobot R1, a machine tool M2, and a robot R2, as shown in FIG. 1.However, the number of manufacturing machines in the manufacturing cell11 is not limited. The manufacturing cell 11 can be a manufacturing linein which a workpiece is successively processed, by a plurality ofmanufacturing machines, into a final product. Alternatively, themanufacturing cell 11 may be a manufacturing line in which two or moreworkpieces (parts) processed in each of two or more manufacturingmachines are combined by another manufacturing machine in the middle ofthe process, in order to obtain a final product. Alternatively, in theinvention of this application, two or more workpieces processed in twoor more manufacturing cells 11 may be combined to obtain a finalproduct.

Each manufacturing machine used in the present invention is not limitedto a NC machine tool or an industrial robot. Examples of eachmanufacturing machine may include a PLC, a transfer machine, a measuringinstrument, a testing device, a press machine, a press fitting machine,a printing machine, a die casting machine, an injection molding machine,a food machine, a packing machine, a welding machine, a washing machine,a painting machine, an assembling machine, a mounting machine, a woodworking machine, a sealing device, or a cutting machine.

Further, it is preferable that the cell controller 12, and the machinetool M1, the robot R1, the machine tool M2, and the robot R2 are eachconstructed of a computer system (not shown) having a memory, such as aROM or RAM, a CPU, and a communication control unit, which areinterconnected via a bus line. Each communication control unit controlsdata passing among the cell controller 12 and the above manufacturingmachines. It is preferable that the functions or operations of the cellcontroller 12 and the above manufacturing machines are each achieved bya program stored in each ROM, which is executed by the correspondingCPU.

The configuration of the cell controller 12 will be described in detail.FIG. 2 is a block diagram specifically illustrating the cell controller12 of the production system 10 shown in FIG. 1.

As shown in FIG. 2, the cell controller 12 according to the presentembodiment includes, as basic components, an inside informationacquiring unit 14, an inside information comparing unit 15, anabnormality cause finding unit 16, and an abnormality cause conveyingunit 17.

The inside information acquiring unit 14 acquires pieces of the insideinformation of a plurality of manufacturing machines 25 to 28constituting the manufacturing cell 11, at given timings. Note that themachine tool M1, the robot R1, the machine tool M2, and the robot R2shown in FIG. 1 respectively correspond to the manufacturing machines 25to 28.

The inside information of each of the manufacturing machines 25 to 28 isthe information stored in a memory of each manufacturing machine, andincludes at least one of a drive parameter, a function parameter, anoperation program, and an operation command log. Note that the memoryfor storing the inside information is not needed to be provided withineach manufacturing machine as in the present embodiment. The memory maybe provided out of each manufacturing machine, or may be provided in thecell controller 12.

Regarding a first manufacturing machine and a second manufacturingmachine, which are selected from the manufacturing machines 25 to 28,the inside information comparing unit 15 compares first insideinformation and second inside information, which are respectivelyacquired from the first manufacturing machine and the secondmanufacturing machine by the inside information acquiring unit 14,thereby extracting a difference therebetween.

The abnormality cause finding unit 16 finds a cause of an abnormalitythat has occurred in the first manufacturing machine or the secondmanufacturing machine, or a cause of an abnormality that may occur inthe future, based on the difference between the first inside informationand the second inside information, which has been extracted by theinside information comparing unit 15. The abnormality cause conveyingunit 17 conveys the cause of the abnormality, which has been found bythe abnormality cause finding unit 16, to the outside of the cellcontroller 12. Examples of the conveying method include a conveyingmethod using, for example, an indicator or a printing device.

In this respect, examples of the drive parameter, the functionparameter, the operation program, and the operation command log, whichare acquired as the inside information of the manufacturing machines,mainly include the following information.

The drive parameter is a parameter directly associated with driving of amanufacturing machine. In, for example, an articulated robot which isoperated by a servomotor as a drive source, the drive parameter includesa pulse count at the mastering position of each axis of a robot, aservo-control parameter, etc. A robot control device controls peripheralequipment, for example, a hand to be driven by a servomotor or a spotwelding gun in some cases, and accordingly, the drive parameter includesparameters associated with the driving of the peripheral equipment.

The function parameter is a parameter which should be set to operate apredetermined function included in a manufacturing machine. Examples ofthe function parameter include network setting information and signalallocating information, which are necessary when a manufacturing machineis connected to a cell controller and peripheral equipment via a fieldnetwork, a software function, and a counter accessible from an operationprogram. Further, the function parameter includes a determinationthreshold value of an abnormality determination function included in amanufacturing machine, for example, the upper limit of torque of aservomotor, which is used to determine that an articulated robotoperated by the servomotor as a drive source receives an excessive load.

The operation program is the information obtained by programming commandprocessing for causing a manufacturing machine to perform apredetermined operation. In, for example, an articulated robot, theoperation program includes information, for example, an operationcommand for moving an arm to an operation position, a command fortransmitting a given signal to the outside, a command for reading thestate of the given signal, etc. The operation program also includes thepositional information of the operation position, and the number of asignal to be operated. Of course, any command, which can be registeredin the program, is acceptable. In short, the present invention is notlimited to the programming information described here as examples.

The operation command log (referred to also as “operation log”) is theinformation obtained by recording, in each time series, key eventsreceived when an operator operates a manufacturing machine, or themanufacturing machine performs a given operation by itself. Examples ofthe operation command log include the log information of a keyoperation, which is input by the operator using an operation board ofthe manufacturing machine, and the screen information displayed on anindicator. Alternatively, the operation command log may be theinformation obtained by arranging, in time series, command processesperformed when the operation program is executed.

As described above, the basic components of the cell controller 12according to the present embodiment has been described. However, it ispreferable that the present invention further includes various kinds ofcomponents that will be described below. In other words, it ispreferable that, as shown in FIG. 2, an abnormal portion correcting unit18, an operation command complementing unit 19, a comparison objectselecting unit 20, an abnormality detecting unit 21, a database 22, adatabase updating unit 23, and a learning instrument 24 are furtherprovided in the cell controller 12. The functions of these componentswill be successively described below.

When at least one of the drive parameter, the function parameter, andthe operation program is a cause of an abnormality, the abnormal portioncorrecting unit 18 corrects a portion having the abnormality. When adeficiency in an operation command to a manufacturing machine is a causeof an abnormality, the operation command complementing unit 19complements the operation command having the deficiency. When, forexample, the difference between the operation command log in the firstinside information and the operation command log in the second insideinformation generates a deficiency of the operation command in the firstor second inside information, the operation command complementing unit19 complements the deficiency of the operation command.

The comparison object selecting unit 20 selects the first manufacturingmachine and the second manufacturing machine, which are to be comparedin the inside information comparing unit 15, from the manufacturingmachines 25 to 28. For example, the comparison object selecting unit 20refers to device configuration information, which is associated witheach manufacturing machine and which represents the components of themanufacturing machine, to compare pieces of the device configurationinformation with each other. Thus, the comparison object selecting unit20 is configured to select the first manufacturing machine in which anabnormality occurs, and the second manufacturing machine havingcomponents similar to those of the first manufacturing machine.Alternatively, the comparison object selecting unit 20 may select thefirst manufacturing machine and the second manufacturing machine, whichare to be compared, based on the registered information obtained bypreviously registering the correlation between the first manufacturingmachine and the second manufacturing machine, which are similar to eachother.

The abnormality detecting unit 21 is configured to detect the occurrenceof an abnormality while monitoring the state of each of themanufacturing machines 25 to 28. The abnormality detecting unit 21 alsoconveys the information on which one of the manufacturing machines hasan abnormality to the comparison object selecting unit 20. Such afunction for detecting the occurrence of an abnormality may be includedin the function of the comparison object selecting unit 20 without beingseparately provided as the abnormality detecting unit 21. Further, it ispreferable that a plurality of sensors (not shown) is provided in eachmanufacturing machine, to detect various conditions of each of themanufacturing machines 25 to 28.

The database 22 is configured to store the state of an abnormality thathas occurred in each manufacturing machine and a cause of the occurrenceof the abnormality, which are correlated with each other. This enablesthe abnormality cause finding unit 16 to find, with reference to anabnormality cause management database, a cause of the abnormality whichoccurs in either the first manufacturing machine or the secondmanufacturing machine, or a cause of an abnormality which may occur inthe future.

The database updating unit 23 is configured to update the informationstored in the database 22. The database updating unit 23 inputs thestate of the abnormality which has occurred in either the firstmanufacturing machine or the second manufacturing machine, and thedifference between the first inside information and the second insideinformation, which is obtained by the inside information comparing unit15 and which causes the abnormality at that time, to the database 22. Incase that the production system 10 according to the present embodimentis provided with a plurality of cell controllers 12, it is preferablethat a database sharing unit (not shown), for example, a server forsharing the database 22 between a cell controller 12 and another cellcontroller 12, is provided.

The learning instrument 24 is provided in the database updating unit 23.The learning instrument 24 learns a combination of the state of theabnormality which has occurred in each manufacturing machine with acause of the occurrence of the abnormality, which is to be stored in thedatabase 22.

However, the abnormal portion correcting unit 18, the operation commandcomplementing unit 19, the comparison object selecting unit 20, theabnormality detecting unit 21, the database 22, the database updatingunit 23, and the learning instrument 24 may be not necessarily providedin the cell controller according to the invention of this application.

The operation of the cell controller 12 according to the presentembodiment will now be described with reference to FIG. 2.

Suppose that an abnormality occurs in the manufacturing machine 26(i.e., the robot R1 shown in FIG. 1) of the manufacturing machines 25 to28. This abnormality is detected by the abnormality detecting unit 21.This causes the comparison object selecting unit 20 to select themanufacturing machine 28 (i.e., the robot R2 shown in FIG. 1) havingcomponents similar to those of the manufacturing machine 26 from amongthe manufacturing machines 25 to 28.

When, in particular, the comparison object selecting unit 20 selects amanufacturing machine similar to the manufacturing machine 26, itdiscriminates the similarity of the manufacturing machine, using theidentification information, e.g., model name registered in eachmanufacturing machine. For example, whether the model name of the robotR1 is identical to the model name of the robot R2 is confirmed.Alternatively, the information that the manufacturing machine 26 and themanufacturing machine 28 have similar configurations may be previouslystored in the cell controller 12, and the similarity between themanufacturing machines may be discriminated. For example, the fact thatthe robot R1 and the robot R2 have similar configurations is previouslyregistered in the comparison object selecting unit 20.

Subsequently, the inside information comparing unit 15 in the cellcontroller 12 compares the same kind of pieces in first insideinformation A acquired from the manufacturing machine 26 and secondinside information B acquired from the manufacturing machine 28, therebyextracting a difference therebetween. Note that the inside informationof the manufacturing machine 26 and the manufacturing machine 28 ispreviously acquired by the inside information acquiring unit 14.

Regarding the timing for acquiring the inside information, the insideinformation is acquired when an abnormality occurs in some embodiments,but it is preferable that the inside information is acquired at apredetermined interval when no abnormality occurs. This is effectivewhen a severe abnormality occurs in a system of a manufacturing machine,and when it is difficult to acquire the inside information at the timeof occurrence of the abnormality. When the inside information of amanufacturing machine is acquired at a predetermined interval, it ispreferable that the inside information comparing unit 15 use the insideinformation acquired at a predetermined period of time before anabnormality occurs.

Subsequently, the abnormality cause finding unit 16 in the cellcontroller 12 analyzes the difference between the first insideinformation A and the second inside information B, which is extractedfrom the inside information comparing unit 15, thereby deciding apossible cause of the abnormality. In this respect, it is preferablethat a possible cause of the abnormality is decided based on theinformation to be compared as in the following examples.

Suppose that, for example, the robot R1 as the manufacturing machine 26abnormally operates, i.e., moves to a position different from thecorrect position for operation. In this instance, an operation programin first inside information A acquired from the manufacturing machine26, i.e., the robot R1 and an operation program in the second insideinformation B acquired from the manufacturing machine 28, i.e., therobot R2 are compared. Consequently, if the positions for operationregistered in the operation programs are different from each other, thisdifference in the position for operation is determined to be a cause ofthe abnormal operation.

Further, suppose that the order of execution of operation programsremaining in the operation command log in the first inside information Ais, from the beginning, operation program no. 1, operation program no.2, and operation program no. 3. In contrast, suppose that the order ofexecution of operation programs remaining in the operational log in thesecond inside information B is, from the beginning, operation programno. 2, operation program no. 1, and operation program no. 3. In thisinstance, the difference in the order of execution of operation programsis determined to be a cause of the abnormal operation.

Besides, any contents of the operation command log, which are to becompared, are acceptable as long as the contents cause some change inthe state of a manufacturing machine, such as the contents of theoperation in an operation board of the manufacturing machine, and theoutput of a signal from the outside.

Further, suppose that the drive parameters in the first insideinformation A and the drive parameters in the second inside informationB are compared, and consequently, there is a difference in a servocontrol parameter, one of the drive parameters, in a given axis. In thisinstance, the difference in the control parameter is determined to be acause of the abnormal operation. Examples of the drive parametersinclude control gains in a motor in each joint axis in an articulatedrobot, mastering counts, and acceleration/deceleration time constants.

The abnormality cause finding unit 16 may refer to the database 22,which correlates and stores the state of an abnormality occurring ineach of the manufacturing machines 25 to 28 and a cause of theoccurrence of the abnormality when deciding a possible cause of theabnormality as described above. It is preferable that the database 22 isnot only used independently in a single cell controller 12 but alsoshared with other databases 22 in a plurality of cell controllers 12 viaa network.

Subsequently, the abnormality cause conveying unit 17 in the cellcontroller 12 conveys the cause of the abnormality, which has been foundby the abnormality cause finding unit 16, to the outside of the cellcontroller 12. In this respect, an output unit (not shown), such as adisplay instrument or a printing device connected to the cell controller12 is conveyed of the cause of the abnormality. Further, the cause ofthe abnormality is conveyed to, for example, the manufacturing machines25 to 28 connected to the cell controller 12 via the communicationdevice 31, or the production management device 13 connected to the cellcontroller 12 via a communication device 32.

The operator recovers the manufacturing machine 26 in which anabnormality occurs, based on the information on the cause of theabnormality, which has been conveyed from the abnormality causeconveying unit 17.

When the manufacturing machine 26 is recovered, the abnormal portioncorrecting unit 18 in the cell controller 12 instead of the operator mayaccess the manufacturing machine 26, in order to correct parameters andthe like which cause an abnormality. Alternatively, if there is adeficiency in the operation procedure of the manufacturing machine 26,the operation command complementing unit 19 in the cell controller 12may automatically complement the operation.

Suppose that, for example, maintenance programs should be periodicallyactivated in two similar robots R1 and R2. In this instance, in somecases, a maintenance program is activated for the robot R1, but amaintenance program is not activated for the robot R2, and the robot R2continues to produce products. In this respect, the inside informationcomparing unit 15 and the abnormality cause finding unit 16 in thepresent embodiment compares the operational logs of the robots R1 and R2before the continuation of production in the robot R2, thereby findingan error in activation of the maintenance program. Then the operationcommand complementing unit 19 automatically activates the maintenanceprogram for the robot R2 before the continuation of production in therobot R2.

It is preferable that, as operations associated with the recovery of themanufacturing machine 26, the state of an abnormality that occurs thistime in the manufacturing machine 26, and the difference between thefirst inside information A and the second inside information B, whichcauses the abnormality, are reflected in the database 22. Such update ofthe information of the database 22 may be performed by the operator'sinput, or may be automatically performed by the database updating unit23.

Further, when the information of the database 22 is automaticallyupdated by the database updating unit 23, it is preferable that thelearning instrument 24 performs machine learning. The learninginstrument 24 performs machine learning, for example, unsupervisedlearning, using the information accumulated in the database 22, theinside information acquired by the inside information acquiring unit 14,or the device configuration information representing the components of amanufacturing machine. Then the database updating unit 23 newlygenerates a correspondence relationship between an abnormality, which isnot registered in the database 22 and does not yet occur, and a cause ofthe abnormality, based on results of the learning, thereby updating thedatabase 22.

Suppose that, for example, an abnormal event, in which a first axis ofthe robot R1, a 6-axis articulated robot, does not normally operate, hasoccurred in the past, and the robot R1 and the robot R2 havingcomponents similar to those of the robot R1 have been compared. Supposethat there is a difference in the servo-control parameter of the firstaxis, as a comparison result at that time, and the difference cause theabnormality. Suppose that the learning instrument 24 obtains a result oflearning, i.e., the fact that the similar abnormal event can occur inaxis other than the first axis, from the information on such adifference, and the device configuration information representing thatthe manufacturing machine is a 6-axis articulated robot. This result oflearning causes the database updating unit 23 to newly register theinformation, in which an abnormal event that a second axis does notnormally operate is caused by the servo-control parameter of the secondaxis, to the database 22.

Further, suppose that machine learning is performed using the operationcommand log in the inside information of the robot R1, and evidence thatthe servo-control parameter of the first axis has been rewritten by theoperator in the past is found. In this instance, another result oflearning, in which direct causes of the abnormal movement include notonly servo-control parameters but also incorrect operations of theoperator, can be obtained. Thus, the database updating unit 23 newlyregisters the information of the incorrect operations to the database22. As described above, a plurality of pieces of information areanalyzed from many directions by machine learning, so that theinformation of the database 22 can be upgraded.

As described above, the cell controller 12 according to the presentembodiment acquires various kinds of inside information of amanufacturing machine, such as parameters or operational logs of themanufacturing machine by communicating with the manufacturing machines25 to 28 via a network. Further, regarding the manufacturing machine 26having an abnormality and the manufacturing machine 28, which is similarto the manufacturing machine 26 and which normally operates, variouskinds of pieces of the inside information are compared, and a differencetherebetween is analyzed. Further, a cause of the abnormality is earlyfound from a result of the analysis, and assistance for quicklyrecovering the manufacturing machine 26 having the abnormality, forexample, alarm output to the outside, correction of an abnormal portion,supplement to an operation command, etc. Such methods enable finding ofa cause of an abnormality which occurs in a manufacturing machine, or acause of an abnormality which may occur in the future, in particular, acause of an abnormality caused by the inside information of amanufacturing machine.

In particular, the inside information acquiring unit 14 periodicallyacquires the inside information of the manufacturing machines 25 to 28in which no abnormality occurs, and the inside information comparingunit 15 successively compares pieces of the inside information of twosimilar manufacturing machines 26 and 28, which has been periodicallyacquired. When the inside information comparing unit 15 finds adifference, which does not yet cause an abnormality, but will probablycause an abnormality in the future, the difference is conveyed to theoutside of the cell controller 12. This prevents an abnormality causedby the inside information of a manufacturing machine from occurring.

The learning instrument 24 (hereinafter referred to as “machine learningapparatus) will now be described in detail. The machine learningapparatus has a function for analytically extracting useful rules orknowledge representations, criteria for determination, etc. from theassembly of data inputted to the apparatus, and a function foroutputting the results of determination, and learning knowledges. Thereare various machine learning methods, and the methods are roughlydivided into “supervised learning”, “unsupervised learning”, and“reinforcement learning”. In order to achieve these leaning methods,there is another method referred to as “deep learning” for learningextraction of feature quantity itself.

“Supervised learning” is a method in which a large volume ofinput-output (label) paired data are given to a machine learningapparatus, so that characteristics of these datasets can be learned, anda model for inferring an output value from input data, i.e., theinput-output relation can be inductively acquired. This can be achievedusing an algorithm, for example, a neural network that will be describedlater.

“Unsupervised learning” is a method in which a large volume ofinput-only data are given to a machine learning apparatus, so that thedistribution of the input data can be learned, and a device for, forexample, compressing, classifying, and fairing the input data can belearned even if the corresponding teacher output data are not given. Forexample, characteristics of these datasets can be clustered based ontheir similarity. The result obtained from the learning is used to set acertain criterion, and then, the allocation of output is performed so asto optimize the criterion, so that the prediction of output can beachieved. There is another problem setting method situated between“unsupervised learning” and “supervised learning”, which is known as“semi-supervised learning”. In this learning method, a small volume ofinput-output paired data and a large volume of input-only data areprovided.

Problems are set in reinforcement learning as follows.

-   -   A machine learning apparatus observes the state of environment,        and decides an action.    -   The environment varies in accordance with some rules, and your        action can vary the environment.    -   A reward signal is returned at each action.    -   The target of maximization is the sum of (discount) rewards to        be obtained now and in the future.    -   Learning starts from the state in which a result caused by an        action is completely unknown, or is incompletely known. The        machine learning apparatus can acquire the result as data only        after it actually starts operating. In other words, it is        necessary to search the optimal action through trial and error.    -   It is also possible to set, as an initial state, the state, in        which a prior learning (e.g., the above supervised learning, or        inverse reinforcement learning) is performed so as to emulate        the action of a person, and start learning from an appropriate        starting point.

“Reinforcement learning” is a learning method for learning not onlydeterminations or classifications but also actions, to learn anappropriate action based on the interaction of environment to an action,i.e., an action to maximize rewards to be obtained in the future. Thisindicates, in the present embodiment, that an action, which can exert aneffect on the future, can be acquired. The explanation of reinforcementlearning will be continued below using, for example, Q-learning, butreinforcement learning is not limited to Q-learning.

Q-learning is a method for learning a value Q(s, a) at which an action ais selected under an environmental state s. In other words, it is onlyrequired that the action a having the highest value Q(s, a) is selectedas an optimal action a, under a given state s. However, initially, thecorrect value of the value Q(s, a) for a combination of the state s andthe action a is completely unknown. Then, the agent (the subject of anaction) selects various actions a under a given state s, and givesrewards to the actions a at that time. Thus, the agent learns selectionof a more beneficial action, i.e., the correct value Q(s, a).

As a result of the action, maximization of the sum of rewards to beobtained in the future is desired, and accordingly, Q(s,a)=E[Σγ^(t)r_(t)] is aimed to be finally achieved (An expected value isset for the time when the state varies in accordance with the optimalaction. As a matter of course, the expected value is unknown, andaccordingly, should be learned while being searched). The updateexpression for such a value Q(s, a) is given, for example, by:

$\begin{matrix} {Q( {s_{t},a_{t}} )}arrow{{Q( {s_{t},a_{t}} )} + {\alpha( {r_{t + 1} + {\gamma{\max\limits_{a}{Q( {s_{t + 1},a} )}}} - {Q( {s_{t},a_{t}} )}} )}}  & {{Equation}\mspace{14mu}(1)}\end{matrix}$

where s_(t) is the state of environment at time t, and a_(t) is theaction at time t. Upon the action a_(t), the state changes to s_(t+1).r_(t+1) is the reward to be received upon a change in the state. Theterm, to which “max” is appended, is obtained by multiplying theQ-value, which is obtained when the action a having the highest Q-valueat that time is selected under the state s_(t+1), by γ. γ is theparameter having a range of 0<γ≤1, and is called discount rate. α is thelearning factor, and has a range of 0<α≤1.

This equation expresses a method for updating an evaluation valueQ(s_(t), a_(t)) of an action a_(t) in a state s_(t) based on a rewardr_(t+1) which has been returned as a result of a trial a_(t). If anevaluation value Q(s_(t+1), max a_(t+1)) of the optimal action max a ina subsequent state caused by the reward r_(t+1)+ the action a is greaterthan the evaluation value Q(s_(t), a_(t)) of the action a in the states, Q(s_(t), a_(t)) is increased. In the contrary case, i.e., theevaluation value Q(s_(t+1), max a_(t+1)) is smaller than the evaluationvalue Q(s_(t), a_(t)), Q(s_(t), a_(t)) is decreased. In other words, thevalue of a given action in a given state is tried to approach the rewardimmediately returned as a result, and the value of an optimal action inthe subsequent state caused by the given action.

Examples of the method for expressing Q(s, a) on a computer include amethod for preserving the values of all state action pairs (s, a) as atable (action-value table), and a method for preparing a function toapproximate Q(s, a). In the latter method, the above update expressioncan be achieved by adjusting a parameter of the approximate functionusing a method, such as stochastic gradient descent. Examples of theapproximate function include a neural network that will be describedlater.

As an approximate algorithm of a value function in supervised learning,unsupervised learning, and reinforcement learning, a neural network canbe used. The neural network is comprised of, for example, an arithmeticdevice and a memory, which realize a neural network simulating a neuronmodel as shown in FIG. 3. FIG. 3 is a schematic diagram illustrating aneuron model.

As shown in FIG. 3, a neuron outputs an output y in response to aplurality of inputs x (inputs x1 to x3 are provided herein as anexample). Weights w (w1 to w3) are applied to the corresponding inputsx1 to x3. This causes the neuron to output the output y that isexpressed by the equation below. Note that the inputs x, the output y,and the weights w are vectors.y=f _(k)(Σ_(i=1) ^(n) x _(i) w _(i)−θ)

where θ is the bias, and f_(k) is the activation function.

A three-layer weighted neural network comprised of a combination ofneurons as described above will now be described below with reference toFIG. 4. FIG. 4 is a schematic diagram illustrating a weighted neuralnetwork having three layers D1 to D3.

As shown in FIG. 4, a plurality of inputs x (inputs x1 to x3 areprovided herein as an example) are inputted from the left side of theneural network, and results y (results y1 to y3 are provided herein asan example) are outputted from the right side of the neural network.

Specifically, the inputs x1 to x3, to which the corresponding weightshave been applied, are respectively inputted to three neurons N11 toN13. These weights applied to the inputs are collectively designated byw1.

The neurons N11 to N13 respectively output z11 to z13. These z11 to z13are collectively designated by a feature vector z1, and can be treatedas a vector obtained by extracting a feature amount of an input vector.This feature vector z1 is a feature vector between the weight w1 and theweight w2.

The feature vectors z11 to z13, to which the corresponding weights havebeen applied, are inputted to two neurons N21 and N22. These weightsapplied to the feature vectors are collectively designated by w2.

The neurons N21 and N22 respectively output z21 and z22. These z21 andz22 are collectively designated by a feature vector z2. This featurevector z2 is a feature vector between the weight w2 and the weight w3.

The feature vectors z21 and z22, to which the corresponding weights havebeen applied, are inputted to three neurons N31 to N33. These weightsapplied to the feature vectors are collectively designated by w3.

Finally, the neurons N31 to N33 respectively output the results y1 toy3.

The operation of the neural network includes a learning mode and a valueprediction mode. A learning dataset is used to learn the weights w inthe learning mode, and parameters obtained from the learning are used todetermine the action of the processing machine in the prediction mode(For convenience, the term “prediction” is used herein, but varioustasks including detection, classification, deduction, etc. can beperformed).

It is possible to perform not only learning (online learning), in whichdata that have been acquired by actually operating the processingmachine in the prediction mode are immediately learned, and arereflected in a subsequent action, but also learning (batch learning), inwhich previously collected data are collectively learned using a groupof the data, and thereafter, a detection mode is performed usingparameters obtained from the learning. Another learning mode can beinterposed every time a predetermined amount of data is collected.

The weights w1 to w3 can be learned by an error back propagation method.The information on errors is introduced from the right side to the leftside. The error back propagation method is a method for adjusting(learning) each weight so as to reduce a difference between the output ywhen the input x is inputted and the true output y (teacher) in eachneuron.

In such a neural network, three or more layers can be provided (This iscalled deep learning). An arithmetic device, which extracts featuresfrom input data, in a stepwise fashion, so as to return a result, can beautomatically acquired from only teacher data.

Note that unsupervised learning, a method of machine learning, isapplied to the learning instrument 24 shown in FIG. 2. Of course, themachine learning method applicable to the learning instrument 24 is notlimited to unsupervised learning. When, for example, supervised learningis applied to the learning instrument 24, the value functions correspondto learning models, and the rewards correspond to errors.

The present invention has been described above using exemplaryembodiments. However, a person skilled in the art would understand thatthe aforementioned modifications and various other modifications,omissions, and additions can be made without departing from the scope ofthe present invention.

Effect of the Invention

According to the first aspect, a cause of an abnormality that occurs ina manufacturing machine, or a cause of an abnormality that may occur inthe future, in particular, a cause of an abnormality caused by theinside information of a manufacturing machine can be efficiently found.According to the second aspect of the present invention, usefulinformation to find a cause of an abnormality can be acquired. Accordingto the third and fourth aspects of the present invention, when a causeof an abnormality is found, a portion which causes the abnormality canbe automatically corrected.

According to the fifth aspect of the present invention, when a cause ofan abnormality in an operational log is found, a lacking operation canbe automatically added. According to the sixth and seventh aspects ofthe present invention, a manufacturing machine to be compared can beeasily selected.

According to the eighth aspect of the present invention, even when asevere abnormality occurs in a manufacturing machine, and the insideinformation acquiring unit cannot acquire the information from themanufacturing machine, the information acquired in a predeterminedperiod of time before the occurrence of the abnormality can be used.

According to the ninth aspect of the present invention, the accuracy infinding a cause of an abnormality can be improved by referring to theinformation of the database. According to the tenth aspect of thepresent invention, the accuracy in finding a cause of an abnormality canbe further improved by updating the database. According to the eleventhaspect of the present invention, the accuracy in finding a cause of anabnormality can be still further improved by sharing the information ofthe database with a plurality of cell controllers. According to thetwelfth aspect of the present invention, the information of the databasecan be spontaneously improved by reflecting the correspondencerelationship between an abnormality of a manufacturing machine and acause of the abnormality, which is obtained from a result of machinelearning, in the database.

What is claimed is:
 1. A cell controller for controlling a plurality ofmanufacturing machines constituting a manufacturing cell, the cellcontroller comprising a processor configured to: acquire insideinformation of the plurality of manufacturing machines, when anabnormality occurs in a first manufacturing machine of the plurality ofmanufacturing machines, select the first manufacturing machine, and fromamong the plurality of manufacturing machines, a second manufacturingmachine which has components similar to those of the first manufacturingmachine and which normally operates, with regard to the selected firstmanufacturing machine and the selected second manufacturing machine,compare acquired first inside information of the first manufacturingmachine and acquired second inside information of the secondmanufacturing machine, and extract a difference between the first insideinformation of the first manufacturing machine and the second insideinformation of the second manufacturing machine, determine a cause ofthe abnormality that occurs in the first manufacturing machine or acause of an abnormality that may occur in the future, based on theextracted difference between the first inside information and the secondinside information, and convey the determined cause of the abnormalityto the outside of the cell controller, wherein the first manufacturingmachine in which the abnormality occurs is recovered based on theconveyed cause of the abnormality.
 2. The cell controller according toclaim 1, wherein the inside information of each manufacturing machineincludes at least one of a drive parameter associated with driving ofsaid each manufacturing machine, a function parameter associated withthe function of said each manufacturing machine, an operation program tobe executed by said each manufacturing machine, and an operation commandlog obtained by recording, in time series, operation commands receivedto cause said each manufacturing machine to perform a predeterminedoperation.
 3. The cell controller according to claim 2, wherein theprocessor is further configured to correct, when at least one of thedrive parameter, the function parameter, and the operation program is acause of the abnormality, a portion having the abnormality.
 4. The cellcontroller according to claim 2, wherein the operation command log isinformation obtained by recording, in time series, operator'soperations, operation program executing processes, input of signals tothe outside, or operation commands generated by input of signals fromthe outside.
 5. The cell controller according to claim 2, wherein theprocessor is further configured to complement, when a lack of either anoperation command log in the first inside information or an operationcommand log in the second inside information causes the abnormality, anoperation according to an operation command lacking in the operationcommand log, with respect to the manufacturing machine which lacks theoperation command log.
 6. The cell controller according to claim 1,wherein the processor is configured to refer to device configurationinformation representing components of each of the manufacturingmachines, and compare the device configuration information of themanufacturing machines with each other for selecting the firstmanufacturing machine and the second manufacturing machine.
 7. The cellcontroller according to claim 1, wherein the processor is configured toselect the first manufacturing machine and the second manufacturingmachine, based on registration information obtained by previouslycorrelating the first manufacturing machine and the second manufacturingmachine, which are similar to each other.
 8. The cell controlleraccording to claim 1, wherein the processor is configured to acquire theinside information of the manufacturing machines at a predeterminedinterval.
 9. The cell controller according to claim 1, furthercomprising: a database which is configured to correlate and store astate of an abnormality that occurs in each of the manufacturingmachines and an cause of the occurrence of the abnormality, wherein theprocessor is configured to refer to the database for finding a cause ofan abnormality that occurs in the first manufacturing machine, or acause of an abnormality that may occur in the future.
 10. The cellcontroller according to claim 9, wherein the processor is furtherconfigured to reflect in the database, the state of an abnormality thatoccurs in either the first manufacturing machine or the secondmanufacturing machine, and the extracted difference between the firstinside information and the second inside information, which causes theabnormality.
 11. The cell controller according to claim 9, wherein thecell controller is one of a plurality of the cell controllers, and theprocessor is further configured to share the database with the othercell controllers.
 12. The cell controller according to claim 9, furthercomprising a learning instrument which is configured to perform machinelearning using the information stored in the database, the acquiredinside information of each of the manufacturing machines, and deviceconfiguration information representing components of each of themanufacturing machines, in order to update the information stored in thedatabase.